I spent last weekend building a local vector cache for my AI coding assistant. It took exactly 46 hours from idea to deployment. The result saves me about $120 a month in API costs and cuts response latency by 60%. Yet, when I mentioned this on Twitter, the reaction was lukewarm at best. Most developers are still obsessed with building new agents or fine-tuning massive models. Nobody seems interested in the boring infrastructure that makes those tools actually usable in production. This is a story about why caching is the most underrated optimization in the AI stack right now. It is also a confession of how I wasted three days over-engineering a solution that should have been simple. The Problem With "Smart" Context By early 2026, every developer uses some form of AI context injection. We dump entire codebases into prompts. We attach documentation PDFs. We paste error logs. The problem is redundancy. I checked my usage logs from March 2026.…